I get the same error:

⋊> /d/c/p/c/e/reporting on master ◦ python model_config.py (longoeixo) 17:48:14
ClearML Task: created new task id=xxx
ClearML results page: xxx
` Any model stored from this point onwards,...

Thank you, now I am getting AttributeError: 'DummyModel' object has no attribute 'model_design' when calling task.update_output_model("best_model.onnx") . I checked the could I thought that it was related to the model not having a config defined, tried to set it with task.set_model_config(cfg.model) but still getting the error.

Solved by removing default parts.

Now I got a strange behavior in which I have 2 tasks on queue, the autoscaler fires two EC2 instances and then turn them off without running the tasks, then It fires two new instances again in a loop.

Thank you, I have defined the AMI manually instead of using the default, now I am getting the following error:

Error: An error occurred (InvalidParameterValue) when calling the RunInstances operation: User data is limited to 16384 bytes

Hi, AgitatedDove14

How do I set the version to 1.5.1,? When I launch the autoscaler the version 1.5.0 is picked by default.

Thank you, I set it, but clearml still creates its own evironment regardless of my environment.yaml.

Let's see if I got how it works on the CLI.
So if I execute:
clearml-data create --name <improved_dataset> --parents <existing_dataset_id>
Where the parent dataset was updated with sync,
I just need to run:
clearml-data upload --id <created_dataset_id>
And the delta will be automatically uploaded to the new dataset?

Thank you, sorry for the daly, I have been playing around with this setup, right now I am generating a requirements.txt with git links to the sibling packages, I had to set on the agent to force the use of the reuqirements.txt. But I am starting to wonder whether It would be easier just changing sys,path on the scripts that use the sibling libs.

Thanks, I thought on doing that, but I was wondering if there was a way to set it into the code.

Thank you for your response, so what is the difference between sync and add? By your description it seems to make no difference whether I added the files via sync or add, since I will have to create a new dataset either way.

Follows the failure part of the log:
` Requirement already satisfied: pip in /root/.clearml/venvs-builds/3.1/lib/python3.10/site-packages (22.2.2)
Collecting Cython
Using cached Cython-0.29.32-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (1.9 MB)
Installing collected packages: Cython
Successfully installed Cython-0.29.32
Collecting boto3==1.24.59
Using cached boto3-1.24.59-py3-none-any.whl (132 kB)
ERROR: Could not find a version that satisfies the requ...

AgitatedDove14 , here follows the full log:

I edited the clearml.conf on the agent and set the manager to poetry, do I need to have poetry installed on the agent beforehand, considering that I am using docker?

Do I have to have the lock file in the root? or it can be on the working dir?

Actually, this error happens when a launch the autoscaler from the Web UI, when I enqueue a task, it launches an EC2 instance which "Status Check" stays in "Pending" for over 15 minutes and then the instance is terminated by the scaler that launches another one in a loop.

Basically, I am following the steps in this video:
https://www.youtube.com/watch?v=j4XVMAaUt3E

I am launching through the UI, "XXX workspace / https://app.clear.ml/applications / AWS Autoscaler".

SuccessfulKoala55 , how do I set the agent version when creating the autoscaler?

I just called the script with:
task.set_base_docker(
docker_image="nvidia/cuda:11.7.0-runtime-ubuntu22.04",
# docker_arguments="--privileged -v /dev:/dev",
)
task.execute_remotely(queue_name="default")

Then in the console:
` Exception: Command '['/usr/bin/python3', '-m', 'poetry', 'config', '--local', 'virtualenvs.in-project', 'true']' returned non-zero exit status 1.
Error: Failed configuring Poetry virtualenvs.in-project
failed installing poetry requirements: Comman...

Tried using a custom python version:

` FROM nvidia/cuda:11.7.0-runtime-ubuntu22.04
ARG DEBIAN_FRONTEND=noninteractive

RUN apt-get update && apt-get install -y
build-essential libssl-dev zlib1g-dev libbz2-dev
libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev
xz-utils tk-dev libffi-dev liblzma-dev git
&& rm -rf /var/lib/apt/lists/*

RUN git clone ~/.pyenv
RUN /root/.pyenv/bin/pyenv install 3.10.6
ENV PATH="/root/.pyenv/versions/3.10....

So downgrading to python 3.8 would be a workaround?

Hi, sorry for the delay. rmdatasets == 0.0.1 is the name of the local package that lives in the same repo as the training code. Instead of picking the relative path to the package.

As as work around I set the setting to force the use of requirements.txt and I am using this script to generate it:

` import os
import subprocess

output = subprocess.check_output(["pip", "freeze"]).decode()

with open("requirements.txt", "w") as f:
for line in output.split("\n"):
if " @" in line...

I think not, I have not set any ENV variable. Just went to the web UI, added an autoscaler, filled the data in the UI and launched the autoscaler.

By inspecting the scaler task, it is running the following docker image: allegroai/clearml-agent-services-app:app-1.1.1-47

This is being started as a command line script.

Also tried saving the model with:
task.set_model_config(cfg.model) task.update_output_model("best_model.onnx")But got the same exception,

Yes, tried with python 3.8, now it works.

Its a S3 bucket, it is working since I am able to upload models before this call and also custom artifacts on the same script.
Ubuntu 18.04
Python: 3.9.5
Clearml: 1.0.4

I have a task that is already completed, and, in other script, I am trying to load it and analyse the results.

Yes, but the issue is caused because rmdatasets is installed in the local environments, I needed it installed in order to test the code locally, so it is caught on the package list.
I will probably stop installing the sibling packages and adding them manually to sys.path.

I used the autogenerated clearml.conf, I will try erasing the unnecessary parts.

I run some tests, I think I got it now.
After creating the new dataset, it is necessary to run sync again, but now only the new files are uploaded.

And when running get the files on the parent dataset will be available as links.